Sun Y X, Lyu J, Shen P, Zhang J Y, Lu P, Huang W Z, Lin H B, Shui L M, Li L M
Department of Data Center, Yinzhou District Center for Disease Control and Prevention, Ningbo 315100, China.
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Zhonghua Liu Xing Bing Xue Za Zhi. 2020 Oct 10;41(10):1611-1615. doi: 10.3760/cma.j.cn112338-20200608-00818.
During the prevention and control of the COVID-19 epidemic, identifying and controlling the source of infection has become one of the most important prevention and control measures to curb the epidemic in the absence of vaccines and specific therapeutic drugs. While actively taking traditional and comprehensive "early detection" measures, Yinzhou district implemented inter-departmental data sharing through the joint prevention and control mechanism. Relying on a healthcare big data platform that integrates the data from medical, disease control and non-health sectors, Yinzhou district innovatively explored the big data-driven COVID-19 case finding pattern with online suspected case screening and offline verification and disposal. Such effort has laid a solid foundation and gathered experience to conduct the dynamic and continuous surveillance and early warning for infectious disease outbreaks more effectively and efficiently in the future. This article introduces the exploration of this pattern in Yinzhou district and discusses the role of big data-driven disease surveillance in the prevention and control of infectious diseases.
在新型冠状病毒肺炎疫情防控期间，在没有疫苗和特效治疗药物的情况下，识别和控制传染源已成为遏制疫情最重要的防控措施之一。鄞州区在积极采取传统的综合性“早发现”措施的同时，通过联防联控机制实现部门间数据共享。依托整合医疗、疾控及非卫生部门数据的医疗卫生大数据平台，鄞州区创新性地探索了以线上疑似病例筛查、线下核实处置为特点的大数据驱动的新冠肺炎病例发现模式。这一举措为今后更有效、高效地开展传染病疫情动态持续监测和预警奠定了坚实基础并积累了经验。本文介绍了鄞州区这一模式的探索情况，并探讨了大数据驱动的疾病监测在传染病防控中的作用。
